{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantile regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This example page shows how to use ``statsmodels``' ``QuantReg`` class to replicate parts of the analysis published in \n",
    "\n",
    "* Koenker, Roger and Kevin F. Hallock. \"Quantile Regression\". Journal of Economic Perspectives, Volume 15, Number 4, Fall 2001, Pages 143–156\n",
    "\n",
    "We are interested in the relationship between income and expenditures on food for a sample of working class Belgian households in 1857 (the Engel data). \n",
    "\n",
    "## Setup\n",
    "\n",
    "We first need to load some modules and to retrieve the data. Conveniently, the Engel dataset is shipped with ``statsmodels``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>foodexp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420.157651</td>\n",
       "      <td>255.839425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>541.411707</td>\n",
       "      <td>310.958667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>901.157457</td>\n",
       "      <td>485.680014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>639.080229</td>\n",
       "      <td>402.997356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>750.875606</td>\n",
       "      <td>495.560775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       income     foodexp\n",
       "0  420.157651  255.839425\n",
       "1  541.411707  310.958667\n",
       "2  901.157457  485.680014\n",
       "3  639.080229  402.997356\n",
       "4  750.875606  495.560775"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = sm.datasets.engel.load_pandas().data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Least Absolute Deviation\n",
    "\n",
    "The LAD model is a special case of quantile regression where q=0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         QuantReg Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                foodexp   Pseudo R-squared:               0.6206\n",
      "Model:                       QuantReg   Bandwidth:                       64.51\n",
      "Method:                 Least Squares   Sparsity:                        209.3\n",
      "Date:                Tue, 24 Dec 2019   No. Observations:                  235\n",
      "Time:                        15:01:50   Df Residuals:                      233\n",
      "                                        Df Model:                            1\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     81.4823     14.634      5.568      0.000      52.649     110.315\n",
      "income         0.5602      0.013     42.516      0.000       0.534       0.586\n",
      "==============================================================================\n",
      "\n",
      "The condition number is large, 2.38e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    }
   ],
   "source": [
    "mod = smf.quantreg('foodexp ~ income', data)\n",
    "res = mod.fit(q=.5)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the results\n",
    "\n",
    "We estimate the quantile regression model for many quantiles between .05 and .95, and compare best fit line from each of these models to Ordinary Least Squares results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for plotting\n",
    "\n",
    "For convenience, we place the quantile regression results in a Pandas DataFrame, and the OLS results in a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      q           a         b        lb        ub\n",
      "0  0.05  124.880096  0.343361  0.268632  0.418090\n",
      "1  0.15  111.693660  0.423708  0.382780  0.464636\n",
      "2  0.25   95.483539  0.474103  0.439900  0.508306\n",
      "3  0.35  105.841294  0.488901  0.457759  0.520043\n",
      "4  0.45   81.083647  0.552428  0.525021  0.579835\n",
      "5  0.55   89.661370  0.565601  0.540955  0.590247\n",
      "6  0.65   74.033435  0.604576  0.582169  0.626982\n",
      "7  0.75   62.396584  0.644014  0.622411  0.665617\n",
      "8  0.85   52.272216  0.677603  0.657383  0.697823\n",
      "9  0.95   64.103964  0.709069  0.687831  0.730306\n",
      "{'a': 147.4753885237058, 'b': 0.4851784236769232, 'lb': 0.45687381301842295, 'ub': 0.5134830343354234}\n"
     ]
    }
   ],
   "source": [
    "quantiles = np.arange(.05, .96, .1)\n",
    "def fit_model(q):\n",
    "    res = mod.fit(q=q)\n",
    "    return [q, res.params['Intercept'], res.params['income']] + \\\n",
    "            res.conf_int().loc['income'].tolist()\n",
    "    \n",
    "models = [fit_model(x) for x in quantiles]\n",
    "models = pd.DataFrame(models, columns=['q', 'a', 'b', 'lb', 'ub'])\n",
    "\n",
    "ols = smf.ols('foodexp ~ income', data).fit()\n",
    "ols_ci = ols.conf_int().loc['income'].tolist()\n",
    "ols = dict(a = ols.params['Intercept'],\n",
    "           b = ols.params['income'],\n",
    "           lb = ols_ci[0],\n",
    "           ub = ols_ci[1])\n",
    "\n",
    "print(models)\n",
    "print(ols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First plot\n",
    "\n",
    "This plot compares best fit lines for 10 quantile regression models to the least squares fit. As Koenker and Hallock (2001) point out, we see that:\n",
    "\n",
    "1. Food expenditure increases with income\n",
    "2. The *dispersion* of food expenditure increases with income\n",
    "3. The least squares estimates fit low income observations quite poorly (i.e. the OLS line passes over most low income households)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(data.income.min(), data.income.max(), 50)\n",
    "get_y = lambda a, b: a + b * x\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for i in range(models.shape[0]):\n",
    "    y = get_y(models.a[i], models.b[i])\n",
    "    ax.plot(x, y, linestyle='dotted', color='grey')\n",
    "    \n",
    "y = get_y(ols['a'], ols['b'])\n",
    "\n",
    "ax.plot(x, y, color='red', label='OLS')\n",
    "ax.scatter(data.income, data.foodexp, alpha=.2)\n",
    "ax.set_xlim((240, 3000))\n",
    "ax.set_ylim((240, 2000))\n",
    "legend = ax.legend()\n",
    "ax.set_xlabel('Income', fontsize=16)\n",
    "ax.set_ylabel('Food expenditure', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second plot\n",
    "\n",
    "The dotted black lines form 95% point-wise confidence band around 10 quantile regression estimates (solid black line). The red lines represent OLS regression results along with their 95% confidence interval.\n",
    "\n",
    "In most cases, the quantile regression point estimates lie outside the OLS confidence interval, which suggests that the effect of income on food expenditure may not be constant across the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = models.shape[0]\n",
    "p1 = plt.plot(models.q, models.b, color='black', label='Quantile Reg.')\n",
    "p2 = plt.plot(models.q, models.ub, linestyle='dotted', color='black')\n",
    "p3 = plt.plot(models.q, models.lb, linestyle='dotted', color='black')\n",
    "p4 = plt.plot(models.q, [ols['b']] * n, color='red', label='OLS')\n",
    "p5 = plt.plot(models.q, [ols['lb']] * n, linestyle='dotted', color='red')\n",
    "p6 = plt.plot(models.q, [ols['ub']] * n, linestyle='dotted', color='red')\n",
    "plt.ylabel(r'$\\beta_{income}$')\n",
    "plt.xlabel('Quantiles of the conditional food expenditure distribution')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
